GPS: a novel group-based phosphorylation predicting and scoring method

doi:10.1016/j.bbrc.2004.11.001

Biochemical and Biophysical Research Communications

Volume 325, Issue 4, 24 December 2004, Pages 1443-1448

https://doi.org/10.1016/j.bbrc.2004.11.001 Get rights and content

Abstract

Protein phosphorylation is an important reversible post-translational modification of proteins, and it orchestrates a variety of cellular processes. Experimental identification of phosphorylation site is labor-intensive and often limited by the availability and optimization of enzymatic reaction. In silico prediction may facilitate the identification of potential phosphorylation sites with ease. Here we present a novel computational method named GPS: group-based phosphorylation site predicting and scoring platform. If two polypeptides differ by only two consecutive amino acids, in particular when the two different amino acids are a conserved pair, e.g., isoleucine (I) and valine (V), or serine (S) and threonine (T), we view these two polypeptides bearing similar 3D structures and biochemical properties. Based on this rationale, we formulated GPS that carries greater computational power with superior performance compared to two existing phosphorylation sites prediction systems, ScanSite 2.0 and PredPhospho. With database in public domain, GPS can predict substrate phosphorylation sites from 52 different protein kinase (PK) families while ScanSite 2.0 and PredPhospho offer at most 30 PK families. Using PKA as a model enzyme, we first compared prediction profiles from the GPS method with those from ScanSite 2.0 and PredPhospho. In addition, we chose an essential mitotic kinase Aurora-B as a model enzyme since ScanSite 2.0 and PredPhospho offer no prediction. However, GPS offers satisfactory sensitivity (94.44%) and specificity (97.14%). Finally, the accuracy of phosphorylation on MCAK predicted by GPS was validated by experimentation, in which six out of seven predicted potential phosphorylation sites on MCAK (Q91636) were experimentally verified. Taken together, we have generated a novel method to predict phosphorylation sites, which offers greater precision and computing power over ScanSite 2.0 and PredPhospho.

Section snippets

Methods

Data collection. We get the data set of phosphorylation sites from Phospho.ELM [24] which also includes the data of PhosphoBase [25]. After removing the phosphorylation sites with ambiguous information of PKs, we get 1404 items. We also manually checked the recent publications and got 597 more items. After clustering some homology PKs with too few known phosphorylation sites into a unique group, we got 52 PK families/PK groups, including ABL, ALK, AMPK, ATM, AURORA-B, BTK, CAK, CAM-II, CDK,

Performance on kinase PKA

We try to evaluate the performance of GPS method against two popular phosphorylation prediction systems, ScanSite 2.0 [20] and PredPhospho [21]. ScanSite 2.0 provides phosphorylation site prediction for 26 kinases, while PredPhospho only provides such functionality for four groups and four families of kinases. Besides most of the kinases in the above two systems, GPS method also includes several kinases which came into focus recently, e.g., Aurora-B. In the following, we will mainly evaluate

Acknowledgments

We thank Dr. T.J. Gibson and Dr. F. Diella for providing the data set of Phospho.ELM for this study. This work was supported by grants from Chinese Natural Science Foundation (39925018 and 30121001), Chinese Academy of Science (KSCX2-2-01), Chinese 973 project (2002CB713700), and American Cancer Society (RPG-99-173-01) to X. Yao. X. Yao is a GCC Distinguished Cancer Research Scholar.

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¹: These authors contributed equally to this work.

View full text

GPS: a novel group-based phosphorylation predicting and scoring method

Abstract

Section snippets

Methods

Performance on kinase PKA

Acknowledgments

J. Biol. Chem.

Biochem. Biophys. Res. Commun.

Biochem. Biophys. Res. Commun.

Biochem. Biophys. Res. Commun.

Biochem. Biophys. Res. Commun.

Biochem. Biophys. Res. Commun.

J. Mol. Biol.

FEBS Lett.

J. Mol. Biol.

Curr. Opin. Genet. Dev.

Curr. Biol.

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Science

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Proc. Natl. Acad. Sci. USA

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